The increasing usage of the Internet by individual users, companies, and other entities, as well as the general increase of available data, has resulted in a collection of data sets that is both large and complex. In particular, the increased prevalence and usage of mobile devices, sensors, software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless networks have led to an increase in available data sets. This collection of data sets is often referred to as “big data.” Because of the size of the big data, existing database management systems and data processing applications are not able to adequately curate, capture, search, store, share, transfer, visualize, or otherwise analyze the big data. Theoretical solutions for big data processing require hardware servers on the order of thousands to adequately process big data, which would result in massive costs and resources for companies and other entities.
Companies, corporations, and the like are starting to feel the pressure to effectively and efficiently process big data. In some cases, users are more often expecting instantaneous access to various information resulting from big data analyses. In other cases, companies feel the need to implement big data processing systems in an attempt to gain an edge on their competitors, as big data analyses can be beneficial to optimizing existing business systems or products as well as implementing new business systems or products. For example, there is a need for insurance providers to analyze big data in an effort to create new insurance products and policies, refine existing insurance products and policies, more accurately price insurance products and policies, process insurance claims, and generally gather more “intelligence” that can ultimately result in lower costs for customers.
Accordingly, there is an opportunity to implement systems and methods for processing big data.